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Osteoporosis is an age-related disease associated with reduced bone density and increased fracture-risk. It is known that bone microstructural quality is a significant determinant of trabecular bone strength and fracture-risk. Emerging CT technology allows high-resolution in vivo imaging at peripheral sites enabling assessment of bone microstructure at low radiation. Resolution dependence of bone microstructural measures together with varying technologies and rapid upgrades in CT scanners warrants data-harmonization in multi-site as well as longitudinal studies. This paper presents an unsupervised deep learning method for high-resolution reconstruction of bone microstructure from low-resolution CT scans using GAN-CIRCLE. The unsupervised training alleviates the need of registered low- and high-resolution images, which is often unavailable. Low- and high-resolution ankle CT scans of twenty volunteers were used for training, validation, and evaluation. Ten thousand unregistered low- and high-resolution patches of size 64×64 were randomly harvested from CT scans of ten volunteers for training and validation. Five thousand matched pairs of low- and highresolution patches were generated for evaluation after registering CT scan pairs from other ten volunteers. Quantitative comparison shows that predicted high-resolution scans have significantly improved structural similarity index (p < 0.01) with true high-resolution scans as compared to the same metric derived from low-resolution data. Also, trabecular bone microstructural measures such as thickness and network area measures computed from predicted high-resolution CT images showed higher (CCC = [0.90, 0.84]) agreement with the reference measures from true high-resolution scans compared to the same measures derived from low-resolution images (CCC = [0.66, 0.83]).
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